Defensive medicine among antibiotic stewards: the international ESCMID AntibioLegalMap survey
Bibliographic record
Abstract
Objectives: To investigate fear of legal claims and defensive behaviours among specialists in infectious diseases (ID) and clinical microbiology (CM) and to identify associated demographic and professional characteristics. Methods: AntibioLegalMap was an international cross-sectional internet-based survey targeting specialists in ID and CM. Three variables were explored: fear of legal liability in antibiotic prescribing/advising on antibiotic prescription; defensive behaviours in antibiotic prescribing; and defensive behaviours in advising. A multivariable logistic regression analysis was performed to identify factors significantly associated with each of the three variables. Results: Eight hundred and thirty individuals from 74 countries participated. Only 0.4% (3/779) had any kind of condemnation for malpractice related to antibiotic prescription. Concerning the fear of liability, 21.2% (164/774) of respondents said they never worried, 45.1% (349/774) sometimes worried and 28.6% (221/774) frequently worried when prescribing/advising on antibiotic prescription. Being female, younger than or equal to 35 years and aware of previous cases of litigation were independently associated with fear. Most respondents (85.0%, 525/618) reported some defensive behaviour in antibiotic prescribing. These behaviours were independently associated with being younger than or equal to 35 years and sometimes or often worried about liability. Similarly, 76.4% (505/661) reported defensive behaviours in advising. These behaviours were associated with being sometimes or often worried about liability. The preferred measures to reduce fear and defensive behaviours were having local guidelines and sharing decisions through teamwork. Conclusions: A significant proportion of specialists in ID and CM reported some form of defensive behaviour in prescribing or advising to prescribe antibiotics. Defensive medicine should be considered when implementing antibiotic stewardship programmes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".